SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
Journal:
arXiv
Published Date:
Mar 16, 2025
Abstract
Generative machine learning models for small molecule drug discovery have
shown immense promise, but many molecules they generate are too difficult to
synthesize, making them impractical for further investigation or development.
In this work, we present a novel approach by fine-tuning Meta's Llama3 Large
Language Models (LLMs) to create SynLlama, which generates full synthetic
pathways made of commonly accessible building blocks and robust organic
reaction templates. SynLlama explores a large synthesizable space using
significantly less data compared to other state-of-the-art methods, and offers
strong performance in bottom-up synthesis, synthesizable analog generation, and
hit expansion, offering medicinal chemists a valuable tool for drug discovery
developments. We find that SynLlama, even without training on external building
blocks, can effectively generalize to unseen yet purchasable building blocks,
meaning that its reconstruction capabilities extend to a broader synthesizable
chemical space than the training data. We also demonstrate the use of SynLlama
in a pharmaceutical context for synthesis planning of analog molecules and hit
expansion leads for proposed inhibitors of target proteins.
